Diagnosis of hypoglycemic episodes using a neural network based rule discovery system
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the cha...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Elsevier
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/15061 |
| _version_ | 1848748793059606528 |
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| author | Chan, Kit Yan Ling, S. Dillon, Tharam Nguyen, H. |
| author_facet | Chan, Kit Yan Ling, S. Dillon, Tharam Nguyen, H. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients’ data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients’ data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients. |
| first_indexed | 2025-11-14T07:10:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-15061 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:10:41Z |
| publishDate | 2011 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-150612019-02-19T04:26:00Z Diagnosis of hypoglycemic episodes using a neural network based rule discovery system Chan, Kit Yan Ling, S. Dillon, Tharam Nguyen, H. hypoglycemic episodes genetic algorithm Neural networks type 1 diabetes mellitus medical diagnosis Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients’ data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients’ data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients. 2011 Journal Article http://hdl.handle.net/20.500.11937/15061 10.1016/j.eswa.2011.02.020 Elsevier fulltext |
| spellingShingle | hypoglycemic episodes genetic algorithm Neural networks type 1 diabetes mellitus medical diagnosis Chan, Kit Yan Ling, S. Dillon, Tharam Nguyen, H. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| title | Diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| title_full | Diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| title_fullStr | Diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| title_full_unstemmed | Diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| title_short | Diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| title_sort | diagnosis of hypoglycemic episodes using a neural network based rule discovery system |
| topic | hypoglycemic episodes genetic algorithm Neural networks type 1 diabetes mellitus medical diagnosis |
| url | http://hdl.handle.net/20.500.11937/15061 |